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Record W4402597501 · doi:10.5206/ijoh.2023.3.17759

Comparing the Homelessness Plan Experiences of Small Canadian Cities: Emerging Insights for Policy and Practice

2024· article· en· W4402597501 on OpenAlexaffvenueabout
K. John Coleman, Stephanie Laing, John R. Graham, Yale D. Belanger, Hélène B. Laramée, Katherine Maurer, Mary Ellen Donnan

Bibliographic record

VenueInternational Journal on Homelessness · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsBishop's UniversityUniversity of LethbridgeMcGill UniversityUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsPlan (archaeology)Political scienceEnvironmental planningPublic administrationRegional scienceEconomic growthGeographyEconomics

Abstract

fetched live from OpenAlex

This paper presents analytical learnings of a community-engaged study in three small to medium-ranged cities in Canada at diverse points in time relative to multi-year plans to end homelessness. Through semi-structured interviews with service providers, this comparative case study provides insight into the experiences of agencies in the homeless-serving sector when adapting and integrating plans to end homelessness in their work. It proposes de-siloing service sectors as an important contribution to systems change theory and organizational change theory in homelessness prevention work, as homelessness-serving agencies in smaller Canadian cities adapt to, and implement, homelessness plans and policies. Findings suggest that jurisdictional issues, unstable and inflexible resources, and communication and data issues impact service providers’ attitudes toward organizational change and their willingness and capacity to adhere to homelessness plans in their communities. While multi-site studies involve economic and political differences across cities, these do not preclude cross-pollination of innovations. Such research offers the potential for cross-site scaling up, and a comparative approach helps systems move beyond biases in policy development and implementation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.090
GPT teacher head0.427
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

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